Source code for pennylane.qnode

# Copyright 2018-2021 Xanadu Quantum Technologies Inc.

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at


# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
This module contains the QNode class and qnode decorator.
# pylint: disable=too-many-instance-attributes,too-many-arguments,protected-access,unnecessary-lambda-assignment, too-many-branches, too-many-statements
import functools
import inspect
import warnings
from import Sequence
from typing import Union
import logging

import pennylane as qml
from pennylane import Device
from pennylane.interfaces import INTERFACE_MAP, SUPPORTED_INTERFACES, set_shots
from pennylane.measurements import CountsMP, MidMeasureMP, Shots
from pennylane.tape import QuantumTape, QuantumScript

logger = logging.getLogger(__name__)

def _convert_to_interface(res, interface):
    Recursively convert res to the given interface.
    interface = INTERFACE_MAP[interface]

    if interface in ["Numpy"]:
        return res

    if isinstance(res, (list, tuple)):
        return type(res)(_convert_to_interface(r, interface) for r in res)

    if isinstance(res, dict):
        return {k: _convert_to_interface(v, interface) for k, v in res.items()}

    return qml.math.asarray(res, like=interface if interface != "tf" else "tensorflow")

# pylint: disable=protected-access
def _get_device_shots(device) -> Shots:
    if isinstance(device, Device):
        if device._shot_vector:
            return Shots(device._raw_shot_sequence)
        return Shots(device.shots)
    return device.shots

[docs]class QNode: """Represents a quantum node in the hybrid computational graph. A *quantum node* contains a :ref:`quantum function <intro_vcirc_qfunc>` (corresponding to a :ref:`variational circuit <glossary_variational_circuit>`) and the computational device it is executed on. The QNode calls the quantum function to construct a :class:`~.QuantumTape` instance representing the quantum circuit. Args: func (callable): a quantum function device (~.Device): a PennyLane-compatible device interface (str): The interface that will be used for classical backpropagation. This affects the types of objects that can be passed to/returned from the QNode. See ``qml.interfaces.SUPPORTED_INTERFACES`` for a list of all accepted strings. * ``"autograd"``: Allows autograd to backpropagate through the QNode. The QNode accepts default Python types (floats, ints, lists, tuples, dicts) as well as NumPy array arguments, and returns NumPy arrays. * ``"torch"``: Allows PyTorch to backpropagate through the QNode. The QNode accepts and returns Torch tensors. * ``"tf"``: Allows TensorFlow in eager mode to backpropagate through the QNode. The QNode accepts and returns TensorFlow ``tf.Variable`` and ``tf.tensor`` objects. * ``"jax"``: Allows JAX to backpropagate through the QNode. The QNode accepts and returns JAX ``Array`` objects. * ``None``: The QNode accepts default Python types (floats, ints, lists, tuples, dicts) as well as NumPy array arguments, and returns NumPy arrays. It does not connect to any machine learning library automatically for backpropagation. * ``"auto"``: The QNode automatically detects the interface from the input values of the quantum function. diff_method (str or .TransformDispatcher): The method of differentiation to use in the created QNode. Can either be a :class:`~.TransformDispatcher`, which includes all quantum gradient transforms in the :mod:`qml.gradients <.gradients>` module, or a string. The following strings are allowed: * ``"best"``: Best available method. Uses classical backpropagation or the device directly to compute the gradient if supported, otherwise will use the analytic parameter-shift rule where possible with finite-difference as a fallback. * ``"device"``: Queries the device directly for the gradient. Only allowed on devices that provide their own gradient computation. * ``"backprop"``: Use classical backpropagation. Only allowed on simulator devices that are classically end-to-end differentiable, for example :class:`default.qubit <~.DefaultQubit>`. Note that the returned QNode can only be used with the machine-learning framework supported by the device. * ``"adjoint"``: Uses an `adjoint method <>`__ that reverses through the circuit after a forward pass by iteratively applying the inverse (adjoint) gate. Only allowed on supported simulator devices such as :class:`default.qubit <~.DefaultQubit>`. * ``"parameter-shift"``: Use the analytic parameter-shift rule for all supported quantum operation arguments, with finite-difference as a fallback. * ``"hadamard"``: Use the analytic hadamard gradient test rule for all supported quantum operation arguments. More info is in the documentation :func:`qml.gradients.hadamard_grad <.gradients.hadamard_grad>`. * ``"finite-diff"``: Uses numerical finite-differences for all quantum operation arguments. * ``"spsa"``: Uses a simultaneous perturbation of all operation arguments to approximate the derivative. * ``None``: QNode cannot be differentiated. Works the same as ``interface=None``. expansion_strategy (str): The strategy to use when circuit expansions or decompositions are required. - ``gradient``: The QNode will attempt to decompose the internal circuit such that all circuit operations are supported by the gradient method. Further decompositions required for device execution are performed by the device prior to circuit execution. - ``device``: The QNode will attempt to decompose the internal circuit such that all circuit operations are natively supported by the device. The ``gradient`` strategy typically results in a reduction in quantum device evaluations required during optimization, at the expense of an increase in classical preprocessing. max_expansion (int): The number of times the internal circuit should be expanded when executed on a device. Expansion occurs when an operation or measurement is not supported, and results in a gate decomposition. If any operations in the decomposition remain unsupported by the device, another expansion occurs. grad_on_execution (bool, str): Whether the gradients should be computed on the execution or not. Only applies if the device is queried for the gradient; gradient transform functions available in ``qml.gradients`` are only supported on the backward pass. The 'best' option chooses automatically between the two options and is default. cache (bool or dict or Cache): Whether to cache evaluations. This can result in a significant reduction in quantum evaluations during gradient computations. If ``True``, a cache with corresponding ``cachesize`` is created for each batch execution. If ``False``, no caching is used. You may also pass your own cache to be used; this can be any object that implements the special methods ``__getitem__()``, ``__setitem__()``, and ``__delitem__()``, such as a dictionary. cachesize (int): The size of any auto-created caches. Only applies when ``cache=True``. max_diff (int): If ``diff_method`` is a gradient transform, this option specifies the maximum number of derivatives to support. Increasing this value allows for higher order derivatives to be extracted, at the cost of additional (classical) computational overhead during the backwards pass. Keyword Args: **kwargs: Any additional keyword arguments provided are passed to the differentiation method. Please refer to the :mod:`qml.gradients <.gradients>` module for details on supported options for your chosen gradient transform. **Example** QNodes can be created by decorating a quantum function: >>> dev = qml.device("default.qubit", wires=1) >>> @qml.qnode(dev) ... def circuit(x): ... qml.RX(x, wires=0) ... return qml.expval(qml.PauliZ(0)) or by instantiating the class directly: >>> def circuit(x): ... qml.RX(x, wires=0) ... return qml.expval(qml.PauliZ(0)) >>> dev = qml.device("default.qubit", wires=1) >>> qnode = qml.QNode(circuit, dev) .. details:: :title: Parameter broadcasting :href: parameter-broadcasting QNodes can be executed simultaneously for multiple parameter settings, which is called *parameter broadcasting* or *parameter batching*. We start with a simple example and briefly look at the scenarios in which broadcasting is possible and useful. Finally we give rules and conventions regarding the usage of broadcasting, together with some more complex examples. Also see the :class:`~.pennylane.operation.Operator` documentation for implementation details. **Example** Again consider the following ``circuit``: >>> dev = qml.device("default.qubit", wires=1) >>> @qml.qnode(dev) ... def circuit(x): ... qml.RX(x, wires=0) ... return qml.expval(qml.PauliZ(0)) If we want to execute it at multiple values ``x``, we may pass those as a one-dimensional array to the QNode: >>> x = np.array([np.pi / 6, np.pi * 3 / 4, np.pi * 7 / 6]) >>> circuit(x) tensor([ 0.8660254 , -0.70710678, -0.8660254 ], requires_grad=True) The resulting array contains the QNode evaluations at the single values: >>> [circuit(x_val) for x_val in x] [tensor(0.8660254, requires_grad=True), tensor(-0.70710678, requires_grad=True), tensor(-0.8660254, requires_grad=True)] In addition to the results being stacked into one ``tensor`` already, the broadcasted execution actually is performed in one simulation of the quantum circuit, instead of three sequential simulations. **Benefits & Supported QNodes** Parameter broadcasting can be useful to simplify the execution syntax with QNodes. More importantly though, the simultaneous execution via broadcasting can be significantly faster than iterating over parameters manually. If we compare the execution time for the above QNode ``circuit`` between broadcasting and manual iteration for an input size of ``100``, we find a speedup factor of about :math:`30`. This speedup is a feature of classical simulators, but broadcasting may reduce the communication overhead for quantum hardware devices as well. A QNode supports broadcasting if all operators that receive broadcasted parameters do so. (Operators that are used in the circuit but do not receive broadcasted inputs do not need to support it.) A list of supporting operators is available in :obj:`~.pennylane.ops.qubit.attributes.supports_broadcasting`. Whether or not broadcasting delivers an increased performance will depend on whether the used device is a classical simulator and natively supports this. The latter can be checked with the capabilities of the device: >>> dev.capabilities()["supports_broadcasting"] True If a device does not natively support broadcasting, it will execute broadcasted QNode calls by expanding the input arguments into separate executions. That is, every device can execute QNodes with broadcasting, but only supporting devices will benefit from it. **Usage** The first example above is rather simple. Broadcasting is possible in more complex scenarios as well, for which it is useful to understand the concept in more detail. The following rules and conventions apply: *There is at most one broadcasting axis* The broadcasted input has (exactly) one more axis than the operator(s) which receive(s) it would usually expect. For example, most operators expect a single scalar input and the *broadcasted* input correspondingly is a 1D array: >>> x = np.array([1., 2., 3.]) >>> op = qml.RX(x, wires=0) # Additional axis of size 3. An operator ``op`` that supports broadcasting indicates the expected number of axes--or dimensions--in its attribute ``op.ndim_params``. This attribute is a tuple with one integer per argument of ``op``. The batch size of a broadcasted operator is stored in ``op.batch_size``: >>> op.ndim_params # RX takes one scalar input. (0,) >>> op.batch_size # The broadcasting axis has size 3. 3 The broadcasting axis is always the leading axis of an argument passed to an operator: >>> from scipy.stats import unitary_group >>> U = np.stack([unitary_group.rvs(4) for _ in range(3)]) >>> U.shape # U stores three two-qubit unitaries, each of shape 4x4 (3, 4, 4) >>> op = qml.QubitUnitary(U, wires=[0, 1]) >>> op.batch_size 3 Stacking multiple broadcasting axes is *not* supported. *Multiple operators are broadcasted simultaneously* It is possible to broadcast multiple parameters simultaneously. In this case, the batch size of the broadcasting axes must match, and the parameters are combined like in Python's ``zip`` function. Non-broadcasted parameters do not need to be augmented manually but can simply be used as one would in individual QNode executions: .. code-block:: python dev = qml.device("default.qubit", wires=4) @qml.qnode(dev) def circuit(x, y, U): qml.QubitUnitary(U, wires=[0, 1, 2, 3]) qml.RX(x, wires=0) qml.RY(y, wires=1) qml.RX(x, wires=2) qml.RY(y, wires=3) return qml.expval(qml.PauliZ(0) @ qml.PauliX(1) @ qml.PauliZ(2) @ qml.PauliZ(3)) x = np.array([0.4, 2.1, -1.3]) y = 2.71 U = np.stack([unitary_group.rvs(16) for _ in range(3)]) This circuit takes three arguments, and the first two are used twice each. ``x`` and ``U`` will lead to a batch size of ``3`` for the ``RX`` rotations and the multi-qubit unitary, respectively. The input ``y`` is a ``float`` value and will be used together with all three values in ``x`` and ``U``. We obtain three output values: >>> circuit(x, y, U) tensor([-0.06939911, 0.26051235, -0.20361048], requires_grad=True) This is equivalent to iterating over all broadcasted arguments using ``zip``: >>> [circuit(x_val, y, U_val) for x_val, U_val in zip(x, U)] [tensor(-0.06939911, requires_grad=True), tensor(0.26051235, requires_grad=True), tensor(-0.20361048, requires_grad=True)] In the same way it is possible to broadcast multiple arguments of a single operator, for example: >>> qml.Rot.ndim_params # Rot takes three scalar arguments (0, 0, 0) >>> x = np.array([0.4, 2.3, -0.1]) # Broadcast the first argument with size 3 >>> y = 1.6 # Do not broadcast the second argument >>> z = np.array([1.2, -0.5, 2.5]) # Broadcast the third argument with size 3 >>> op = qml.Rot(x, y, z, wires=0) >>> op.batch_size 3 *Broadcasting does not modify classical processing* Note that classical processing in QNodes will happen *before* broadcasting is taken into account. This means, that while *operators* always interpret the first axis as the broadcasting axis, QNodes do not necessarily do so: .. code-block:: python @qml.qnode(dev) def circuit_unpacking(x): qml.RX(x[0], wires=0) qml.RY(x[1], wires=1) qml.RZ(x[2], wires=1) return qml.expval(qml.PauliZ(0) @ qml.PauliX(1)) x = np.array([[1, 2], [3, 4], [5, 6]]) The prepared parameter ``x`` has shape ``(3, 2)``, corresponding to the three operations and a batch size of ``2``: >>> circuit_unpacking(x) tensor([0.02162852, 0.30239696], requires_grad=True) If we were to iterate manually over the parameter settings, we probably would put the batching axis in ``x`` first. This is not the behaviour with parameter broadcasting because it does not modify the unpacking step within the QNode, so that ``x`` is unpacked *first* and the unpacked elements are expected to contain the broadcasted parameters for each operator individually; if we attempted to put the broadcasting axis of size ``2`` first, the indexing of ``x`` would fail in the ``RZ`` rotation within the QNode. """ def __init__( self, func, device: Union[Device, "qml.devices.Device"], interface="auto", diff_method="best", expansion_strategy="gradient", max_expansion=10, grad_on_execution="best", cache=True, cachesize=10000, max_diff=1, **gradient_kwargs, ): if logger.isEnabledFor(logging.DEBUG): logger.debug( """Creating QNode(func=%s, device=%s, interface=%s, diff_method=%s, expansion_strategy=%s, max_expansion=%s, grad_on_execution=%s, cache=%s, cachesize=%s, max_diff=%s, gradient_kwargs=%s""", func if not (logger.isEnabledFor(qml.logging.TRACE) and inspect.isfunction(func)) else "\n" + inspect.getsource(func), repr(device), interface, diff_method, expansion_strategy, max_expansion, grad_on_execution, cache, cachesize, max_diff, gradient_kwargs, ) if interface not in SUPPORTED_INTERFACES: raise qml.QuantumFunctionError( f"Unknown interface {interface}. Interface must be " f"one of {SUPPORTED_INTERFACES}." ) if not isinstance(device, (Device, qml.devices.Device)): raise qml.QuantumFunctionError( "Invalid device. Device must be a valid PennyLane device." ) if "shots" in inspect.signature(func).parameters: warnings.warn( "Detected 'shots' as an argument to the given quantum function. " "The 'shots' argument name is reserved for overriding the number of shots " "taken by the device. Its use outside of this context should be avoided.", UserWarning, ) self._qfunc_uses_shots_arg = True else: self._qfunc_uses_shots_arg = False for kwarg in gradient_kwargs: if kwarg in ["gradient_fn", "grad_method"]: warnings.warn( f"It appears you may be trying to set the method of differentiation via the kwarg " f"{kwarg}. This is not supported in qnode and will default to backpropogation. Use " f"diff_method instead." ) elif kwarg not in qml.gradients.SUPPORTED_GRADIENT_KWARGS: warnings.warn( f"Received gradient_kwarg {kwarg}, which is not included in the list of standard qnode " f"gradient kwargs." ) # input arguments self.func = func self.device = device self._interface = interface self.diff_method = diff_method self.expansion_strategy = expansion_strategy self.max_expansion = max_expansion # execution keyword arguments self.execute_kwargs = { "grad_on_execution": grad_on_execution, "cache": cache, "cachesize": cachesize, "max_diff": max_diff, "max_expansion": max_expansion, } if self.expansion_strategy == "device": self.execute_kwargs["expand_fn"] = None # internal data attributes self._tape = None self._qfunc_output = None self._user_gradient_kwargs = gradient_kwargs self._original_device = device self.gradient_fn = None self.gradient_kwargs = {} self._tape_cached = False self._update_gradient_fn() functools.update_wrapper(self, func) self._transform_program = qml.transforms.core.TransformProgram() def __copy__(self): copied_qnode = QNode.__new__(QNode) for attr, value in vars(self).items(): if attr not in {"execute_kwargs", "_transform_program", "gradient_kwargs"}: setattr(copied_qnode, attr, value) copied_qnode.execute_kwargs = dict(self.execute_kwargs) copied_qnode._transform_program = qml.transforms.core.TransformProgram( self.transform_program ) # pylint: disable=protected-access copied_qnode.gradient_kwargs = dict(self.gradient_kwargs) return copied_qnode def __repr__(self): """String representation.""" if isinstance(self.device, qml.devices.Device): return f"<QNode: device='{self.device}', interface='{self.interface}', diff_method='{self.diff_method}'>" detail = "<QNode: wires={}, device='{}', interface='{}', diff_method='{}'>" return detail.format( self.device.num_wires, self.device.short_name, self.interface, self.diff_method, ) @property def interface(self): """The interface used by the QNode""" return self._interface @interface.setter def interface(self, value): if value not in SUPPORTED_INTERFACES: raise qml.QuantumFunctionError( f"Unknown interface {value}. Interface must be one of {SUPPORTED_INTERFACES}." ) self._interface = INTERFACE_MAP[value] self._update_gradient_fn() @property def transform_program(self): """The transform program used by the QNode.""" return self._transform_program
[docs] def add_transform(self, transform_container): """Add a transform (container) to the transform program. .. warning:: This is a developer facing feature and is called when a transform is applied on a QNode. """ self._transform_program.push_back(transform_container=transform_container)
def _update_gradient_fn(self, shots=None): if self.diff_method is None: self._interface = None self.gradient_fn = None self.gradient_kwargs = {} return if self.interface == "auto" and self.diff_method in ["backprop", "best"]: if self.diff_method == "backprop": # Check that the device has the capabilities to support backprop if isinstance(self.device, Device): backprop_devices = self.device.capabilities().get("passthru_devices", None) if backprop_devices is None: raise qml.QuantumFunctionError( f"The {self.device.short_name} device does not support native computations with " "autodifferentiation frameworks." ) return self.gradient_fn, self.gradient_kwargs, self.device = self.get_gradient_fn( self._original_device, self.interface, self.diff_method, shots=shots ) self.gradient_kwargs.update(self._user_gradient_kwargs or {}) def _update_original_device(self): # FIX: If the qnode swapped the device, increase the num_execution value on the original device. # In the long run, we should make sure that the user's device is the one # actually run so she has full control. This could be done by changing the class # of the user's device before and after executing the tape. if self.device is not self._original_device: if not self._tape_cached: self._original_device._num_executions += 1 # pylint: disable=protected-access # Update for state vector simulators that have the _pre_rotated_state attribute if hasattr(self._original_device, "_pre_rotated_state"): self._original_device._pre_rotated_state = self.device._pre_rotated_state # Update for state vector simulators that have the _state attribute if hasattr(self._original_device, "_state"): self._original_device._state = self.device._state # pylint: disable=too-many-return-statements
[docs] @staticmethod def get_gradient_fn(device, interface, diff_method="best", shots=None): """Determine the best differentiation method, interface, and device for a requested device, interface, and diff method. Args: device (.Device): PennyLane device interface (str): name of the requested interface diff_method (str or .TransformDispatcher): The requested method of differentiation. If a string, allowed options are ``"best"``, ``"backprop"``, ``"adjoint"``, ``"device"``, ``"parameter-shift"``, ``"hadamard"``, ``"finite-diff"``, or ``"spsa"``. A gradient transform may also be passed here. Returns: tuple[str or .TransformDispatcher, dict, .Device: Tuple containing the ``gradient_fn``, ``gradient_kwargs``, and the device to use when calling the execute function. """ if diff_method == "best": return QNode.get_best_method(device, interface, shots=shots) if diff_method == "backprop": return QNode._validate_backprop_method(device, interface, shots=shots) if diff_method == "adjoint": return QNode._validate_adjoint_method(device) if diff_method == "device": return QNode._validate_device_method(device) if diff_method == "parameter-shift": return QNode._validate_parameter_shift(device) if diff_method == "finite-diff": return qml.gradients.finite_diff, {}, device if diff_method == "spsa": return qml.gradients.spsa_grad, {}, device if diff_method == "hadamard": return qml.gradients.hadamard_grad, {}, device if isinstance(diff_method, str): raise qml.QuantumFunctionError( f"Differentiation method {diff_method} not recognized. Allowed " "options are ('best', 'parameter-shift', 'backprop', 'finite-diff', " "'device', 'adjoint', 'spsa', 'hadamard')." ) if isinstance(diff_method, qml.transforms.core.TransformDispatcher): return diff_method, {}, device raise qml.QuantumFunctionError( f"Differentiation method {diff_method} must be a gradient transform or a string." )
[docs] @staticmethod def get_best_method(device, interface, shots=None): """Returns the 'best' differentiation method for a particular device and interface combination. This method attempts to determine support for differentiation methods using the following order: * ``"device"`` * ``"backprop"`` * ``"parameter-shift"`` * ``"finite-diff"`` The first differentiation method that is supported (going from top to bottom) will be returned. Note that the SPSA-based and Hadamard-based gradients are not included here. Args: device (.Device): PennyLane device interface (str): name of the requested interface Returns: tuple[str or .TransformDispatcher, dict, .Device: Tuple containing the ``gradient_fn``, ``gradient_kwargs``, and the device to use when calling the execute function. """ try: return QNode._validate_device_method(device) except qml.QuantumFunctionError: try: return QNode._validate_backprop_method(device, interface, shots=shots) except qml.QuantumFunctionError: try: return QNode._validate_parameter_shift(device) except qml.QuantumFunctionError: return qml.gradients.finite_diff, {}, device
[docs] @staticmethod def best_method_str(device, interface): """Similar to :meth:`~.get_best_method`, except return the 'best' differentiation method in human-readable format. This method attempts to determine support for differentiation methods using the following order: * ``"device"`` * ``"backprop"`` * ``"parameter-shift"`` * ``"finite-diff"`` The first differentiation method that is supported (going from top to bottom) will be returned. Note that the SPSA-based and Hadamard-based gradient are not included here. This method is intended only for debugging purposes. Otherwise, :meth:`~.get_best_method` should be used instead. Args: device (.Device): PennyLane device interface (str): name of the requested interface Returns: str: The gradient function to use in human-readable format. """ transform = QNode.get_best_method(device, interface)[0] if transform is qml.gradients.finite_diff: return "finite-diff" if transform in (qml.gradients.param_shift, qml.gradients.param_shift_cv): return "parameter-shift" # only other options at this point are "backprop" or "device" return transform
@staticmethod def _validate_backprop_method(device, interface, shots=None): if shots is not None or _get_device_shots(device): raise qml.QuantumFunctionError("Backpropagation is only supported when shots=None.") if isinstance(device, qml.devices.Device): config = qml.devices.ExecutionConfig(gradient_method="backprop", interface=interface) if device.supports_derivatives(config): return "backprop", {}, device raise qml.QuantumFunctionError( f"Device {} does not support backprop with {config}" ) mapped_interface = INTERFACE_MAP.get(interface, interface) # determine if the device supports backpropagation backprop_interface = device.capabilities().get("passthru_interface", None) if backprop_interface is not None: # device supports backpropagation natively if mapped_interface == backprop_interface: return "backprop", {}, device raise qml.QuantumFunctionError( f"Device {device.short_name} only supports diff_method='backprop' when using the " f"{backprop_interface} interface." ) # determine if the device has any child devices that support backpropagation backprop_devices = device.capabilities().get("passthru_devices", None) if backprop_devices is not None: # device is analytic and has child devices that support backpropagation natively if mapped_interface in backprop_devices: # no need to create another device if the child device is the same (e.g., default.mixed) if backprop_devices[mapped_interface] == device.short_name: return "backprop", {}, device # TODO: need a better way of passing existing device init options # to a new device? expand_fn = device.expand_fn batch_transform = device.batch_transform device = qml.device( backprop_devices[mapped_interface], wires=device.wires, shots=device.shots ) device.expand_fn = expand_fn device.batch_transform = batch_transform return "backprop", {}, device raise qml.QuantumFunctionError( f"Device {device.short_name} only supports diff_method='backprop' when using the " f"{list(backprop_devices.keys())} interfaces." ) raise qml.QuantumFunctionError( f"The {device.short_name} device does not support native computations with " "autodifferentiation frameworks." ) @staticmethod def _validate_adjoint_method(device): # The conditions below provide a minimal set of requirements that we can likely improve upon in # future, or alternatively summarize within a single device capability. Moreover, we also # need to inspect the circuit measurements to ensure only expectation values are taken. This # cannot be done here since we don't yet know the composition of the circuit. if isinstance(device, qml.devices.Device): config = qml.devices.ExecutionConfig( gradient_method="adjoint", use_device_gradient=True ) if device.supports_derivatives(config): return "adjoint", {}, device raise ValueError(f"The {device} device does not support adjoint differentiation.") required_attrs = ["_apply_operation", "_apply_unitary", "adjoint_jacobian"] supported_device = all(hasattr(device, attr) for attr in required_attrs) supported_device = supported_device and device.capabilities().get("returns_state") if not supported_device: raise ValueError( f"The {device.short_name} device does not support adjoint differentiation." ) if device.shots is not None: warnings.warn( "Requested adjoint differentiation to be computed with finite shots. " "Adjoint differentiation always calculated exactly.", UserWarning, ) return "device", {"use_device_state": True, "method": "adjoint_jacobian"}, device @staticmethod def _validate_device_method(device): if isinstance(device, Device): # determine if the device provides its own jacobian method if device.capabilities().get("provides_jacobian", False): return "device", {}, device name = device.short_name else: config = qml.devices.ExecutionConfig(gradient_method="device") if device.supports_derivatives(config): return "device", {}, device name = raise qml.QuantumFunctionError( f"The {name} device does not provide a native " "method for computing the jacobian." ) @staticmethod def _validate_parameter_shift(device): if isinstance(device, qml.devices.Device): return qml.gradients.param_shift, {}, device model = device.capabilities().get("model", None) if model in {"qubit", "qutrit"}: return qml.gradients.param_shift, {}, device if model == "cv": return qml.gradients.param_shift_cv, {"dev": device}, device raise qml.QuantumFunctionError( f"Device {device.short_name} uses an unknown model ('{model}') " "that does not support the parameter-shift rule." ) @property def tape(self) -> QuantumTape: """The quantum tape""" return self._tape qtape = tape # for backwards compatibility
[docs] def construct(self, args, kwargs): # pylint: disable=too-many-branches """Call the quantum function with a tape context, ensuring the operations get queued.""" old_interface = self.interface if self._qfunc_uses_shots_arg: shots = _get_device_shots(self._original_device) else: shots = kwargs.pop("shots", _get_device_shots(self._original_device)) if old_interface == "auto": self.interface = qml.math.get_interface(*args, *list(kwargs.values())) with qml.queuing.AnnotatedQueue() as q: self._qfunc_output = self.func(*args, **kwargs) self._tape = QuantumScript.from_queue(q, shots) params = self.tape.get_parameters(trainable_only=False) self.tape.trainable_params = qml.math.get_trainable_indices(params) if any(isinstance(m, CountsMP) for m in self.tape.measurements) and any( qml.math.is_abstract(a) for a in args ): raise qml.QuantumFunctionError("Can't JIT a quantum function that returns counts.") if isinstance(self._qfunc_output, qml.numpy.ndarray): measurement_processes = tuple(self.tape.measurements) elif not isinstance(self._qfunc_output, Sequence): measurement_processes = (self._qfunc_output,) else: measurement_processes = self._qfunc_output if not measurement_processes or not all( isinstance(m, qml.measurements.MeasurementProcess) for m in measurement_processes ): raise qml.QuantumFunctionError( "A quantum function must return either a single measurement, " "or a nonempty sequence of measurements." ) terminal_measurements = [ m for m in self.tape.measurements if not isinstance(m, MidMeasureMP) ] if any(ret is not m for ret, m in zip(measurement_processes, terminal_measurements)): raise qml.QuantumFunctionError( "All measurements must be returned in the order they are measured." ) num_wires = len(self.tape.wires) if not self.device.wires else len(self.device.wires) for obj in self.tape.operations + self.tape.observables: if ( getattr(obj, "num_wires", None) is qml.operation.WiresEnum.AllWires and obj.wires and len(obj.wires) != num_wires ): # check here only if enough wires raise qml.QuantumFunctionError(f"Operator {} must act on all wires") # pylint: disable=no-member if isinstance(obj, qml.ops.qubit.SparseHamiltonian) and self.gradient_fn == "backprop": raise qml.QuantumFunctionError( "SparseHamiltonian observable must be used with the parameter-shift " "differentiation method" ) # Apply the deferred measurement principle if the device doesn't # support mid-circuit measurements natively. # Only apply transform with old device API as postselection with # broadcasting will split tapes. expand_mid_measure = ( any(isinstance(op, MidMeasureMP) for op in self.tape.operations) and not isinstance(self.device, qml.devices.Device) and not self.device.capabilities().get("supports_mid_measure", False) ) if expand_mid_measure: # Assume that tapes are not split if old device is used since postselection is not supported. tapes, _ = qml.defer_measurements(self._tape, device=self.device) self._tape = tapes[0] if self.expansion_strategy == "device": if isinstance(self.device, qml.devices.Device): tape, _ = self.device.preprocess()[0]([self.tape]) if len(tape) != 1: raise ValueError( "Using 'device' for the `expansion_strategy` is not supported for batches of tapes" ) self._tape = tape[0] else: self._tape = self.device.expand_fn(self.tape, max_expansion=self.max_expansion) if old_interface == "auto": self.interface = "auto"
[docs] def __call__(self, *args, **kwargs) -> qml.typing.Result: override_shots = False old_interface = self.interface if old_interface == "auto": self.interface = qml.math.get_interface(*args, *list(kwargs.values())) self.device.tracker = self._original_device.tracker if not self._qfunc_uses_shots_arg: # If shots specified in call but not in qfunc signature, # interpret it as device shots value for this call. override_shots = kwargs.get("shots", False) if override_shots is not False: # Since shots has changed, we need to update the preferred gradient function. # This is because the gradient function chosen at initialization may # no longer be applicable. # store the initialization gradient function original_grad_fn = [self.gradient_fn, self.gradient_kwargs, self.device] # pylint: disable=not-callable # update the gradient function if isinstance(self._original_device, qml.Device): set_shots(self._original_device, override_shots)(self._update_gradient_fn)( shots=override_shots ) else: self._update_gradient_fn(shots=override_shots) else: kwargs["shots"] = _get_device_shots(self._original_device) # construct the tape self.construct(args, kwargs) cache = self.execute_kwargs.get("cache", False) using_custom_cache = ( hasattr(cache, "__getitem__") and hasattr(cache, "__setitem__") and hasattr(cache, "__delitem__") ) self._tape_cached = using_custom_cache and self.tape.hash in cache config = None # Add the device program to the QNode program if isinstance(self.device, qml.devices.Device): if self.gradient_fn is None: _gradient_method = None elif isinstance(self.gradient_fn, str): _gradient_method = self.gradient_fn else: _gradient_method = "gradient-transform" grad_on_execution = self.execute_kwargs.get("grad_on_execution") config = qml.devices.ExecutionConfig( interface=self.interface, gradient_method=_gradient_method, grad_on_execution=None if grad_on_execution == "best" else grad_on_execution, ) device_transform_program, config = self.device.preprocess(execution_config=config) full_transform_program = self.transform_program + device_transform_program else: full_transform_program = qml.transforms.core.TransformProgram(self.transform_program) # Add the gradient expand to the program if necessary if ( isinstance(self.gradient_fn, qml.transforms.core.TransformDispatcher) and self.gradient_fn.expand_transform ): full_transform_program.insert_front_transform( qml.transforms.core.TransformDispatcher(self.gradient_fn.expand_transform), **self.gradient_kwargs, ) # Calculate the classical jacobians if necessary if full_transform_program.has_classical_cotransform(): hybrid = full_transform_program[-1]._kwargs.pop( "hybrid", True ) # pylint: disable=protected-access if hybrid: argnums = full_transform_program[-1]._kwargs.pop( "argnums", None ) # pylint: disable=protected-access full_transform_program._set_all_classical_jacobians( self, args, kwargs, argnums ) # pylint: disable=protected-access full_transform_program._set_all_argnums( self, args, kwargs, argnums ) # pylint: disable=protected-access # pylint: disable=unexpected-keyword-arg res = qml.execute( (self._tape,), device=self.device, gradient_fn=self.gradient_fn, interface=self.interface, transform_program=full_transform_program, config=config, gradient_kwargs=self.gradient_kwargs, override_shots=override_shots, **self.execute_kwargs, ) res = res[0] # convert result to the interface in case the qfunc has no parameters if ( len(self.tape.get_parameters(trainable_only=False)) == 0 and not self.transform_program.is_informative ): res = _convert_to_interface(res, self.interface) if old_interface == "auto": self.interface = "auto" # Special case of single Measurement in a list if isinstance(self._qfunc_output, list) and len(self._qfunc_output) == 1: return [res] # If the return type is not tuple (list or ndarray) (Autograd and TF backprop removed) if not isinstance(self._qfunc_output, (tuple, qml.measurements.MeasurementProcess)): has_partitioned_shots = ( self.tape.shots.has_partitioned_shots if isinstance(self.device, qml.devices.Device) else self.device._shot_vector ) if has_partitioned_shots: res = [type(self._qfunc_output)(r) for r in res] res = tuple(res) else: res = type(self._qfunc_output)(res) if override_shots is not False: # restore the initialization gradient function self.gradient_fn, self.gradient_kwargs, self.device = original_grad_fn self._update_original_device() return res
qnode = lambda device, **kwargs: functools.partial(QNode, device=device, **kwargs) qnode.__doc__ = QNode.__doc__ qnode.__signature__ = inspect.signature(QNode)